Biomedical Engineering Reference
In-Depth Information
bidity are estimated to be 3
.
2 % (Vlak et al.,
2011
). Most remain asymptomatic;
however, there is a small but inherent risk of rupture: 0
.
1 to 1 % of detected
aneurysms rupture every year (Juvela,
2004
). Subarachnoid haemorrhage (SAH)
due to IA rupture is associated with a 50 % chance of fatality (Greving et al.,
2009
)
and of those that survive, nearly half have long term physical and mental sequelae
(Huang et al.,
2011
). Preemptive treatment may prevent aneurysm SAH and thus
reduce the associated (large) financial burden, e.g., the total annual economic cost
of aneurysm SAH is £510 M in the UK (Rivero-Arias et al.,
2010
). However, man-
agement of unruptured IAs by interventional procedures, i.e. minimally invasive
endovascular approaches or surgical-clipping, is highly controversial and not with-
out risk (Komotar et al.,
2008
). Given the very low risk of IA rupture, there is both
a clinical and an economic need to identify those IAs which are actually in need of
intervention.
Investigating the complex interplay of physical forces and their biological se-
quelae will aid further understanding of the formation and rupture of IAs and their
management, and may lead to a cure (Krings et al.,
2011
). However, IAs may have
heterogeneous hemodynamic, morphologic, and vascular characteristics associated
with different mechanistic pathways (Sugiyama et al.,
2011
) and thus this is an
extremely challenging complex problem. Computational models may yield insight
into the aetiology of the disease and offer the potential to aid clinical decisions.
Consequently, research in this area has grown extensively; for recent review articles
see, e.g., Humphrey (
2009
); Sforza et al. (
2011
). In this article, we briefly review
computational models which investigate IA inception and IA evolution and present
our most recent models and findings on these topical areas of research.
12.2 IA Inception
IAs preferentially develop at specific locations in the Circle of Willis. Hence it
appears that the hemodynamic environment plays a role in the pathophysiologi-
cal processes that give rise to their formation. This has motivated the development
of methodologies to reconstruct the original
healthy
geometry of the vasculature
from a
diseased
geometry depicting an IA: computational fluid dynamic (CFD) an-
alyzes proceed to investigate if particular patterns of hemodynamic stimuli (on the
healthy vasculature) correlate with the location at which an IA is observed to de-
velop, e.g., see the recent studies by Mantha et al. (
2006
); Baek et al. (
2009
); Ford
et al. (
2009
); Shimogonya et al. (
2009
); Singh et al. (
2010
). Whilst all these studies
have considered a limited number of clinical cases, i.e. between 1 and 5, interesting
(although somewhat inconsistent) observations have been made (see Table
12.1
).
It has been concluded that locations susceptible to aneurysm formation are associ-
ated with: oscillatory wall shear stress (WSS) indicated by a novel index referred to
as the aneurysm formation index (AFI) (Mantha et al.,
2006
); large temporal fluc-
tuations of the direction of the spatial WSS gradient vector (WSSG) indicated by
a novel index referred to as the gradient oscillatory number (GON) (Shimogonya
